• Title/Summary/Keyword: inherent optical property (IOP)

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Atmospheric and BRDF Correction Method for Geostationary Ocean Color Imagery (GOCI) (정지궤도 해색탑재체(GOCI) 자료를 위한 대기 및 BRDF 보정 연구)

  • Min, Jee-Eun;Ryu, Joo-Hyung;Ahn, Yu-Hwan;Palanisamy, Shanmugam;Deschamps, Pierre-Yves;Lee, Zhong-Ping
    • Korean Journal of Remote Sensing
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    • v.26 no.2
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    • pp.175-188
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    • 2010
  • A new correction method is required for the Geostationary Ocean Color Imager (GOCI), which is the world's first ocean color observing sensor in geostationary orbit. In this paper we introduce a new method of atmospheric and the Bidirectional Reflectance Distribution Function(BRDF) correction for GOCI. The Spectral Shape Matching Method(SSMM) and the Sun Glint Correction Algorithm(SGCA) were developed for atmospheric correction, and BRDF correction was improved using Inherent Optical Property(IOP) data. Each method was applied to the Sea-Viewing Wide Field-of-view Sensor(SeaWiFS) images obtained in the Korean sea area. More accurate estimates of chlorophyll concentrations could be possible in the turbid coastal waters as well as areas severely affected by aerosols.

Sensitivity Analysis of Volcanic Ash Inherent Optical Properties to the Remote Sensed Radiation (화산재입자의 고유 광학특성이 원격탐사 복사량에 미치는 민감도 분석)

  • Lee, Kwon-Ho;Jang, Eun-Suk
    • Korean Journal of Remote Sensing
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    • v.30 no.1
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    • pp.47-59
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    • 2014
  • Volcanic ash (VA) can be estimated by remote sensing sensors through their spectral signatures determined by the inherent optical property (IOP) including complex refractive index and the scattering properties. Until now, a very limited range of VA refractive indices has been reported and the VA from each volcanic eruption has a different composition. To improve the robustness of VA remote sensing, there is a need to understanding of VA - radiation interactions. In this study, we calculated extinction coefficient, scattering phase function, asymmetry factor, and single scattering albedo which show different values between andesite and pumice. Then, IOPs were used to analyze the relationship between theoretical remote sensed radiation calculated by radiative transfer model under various aerosol optical thickness (${\tau}$) and sun-sensor geometries and characteristics of VA. It was found that the mean rate of change of radiance at top of atmosphere versus ${\tau}$ is six times larger than in radiance values at 0.55 ${\mu}m$. At the surface, positive correlation dominates when ${\tau}$ <1, but negative correlation dominates when ${\tau}$ >1. However, radiance differences between andesite and pumice at 11 ${\mu}m$ are very small. These differences between two VA types are expressed as the polynomial regression functions and that increase as VA optical thickness increases. Finally, these results would allow VA to be better characterized by remote sensing sensors.

STUDY ON THE DEVELOPMENT OF $a_{dom}$ ESTIMATION ALGORITHM BY EMPIRICAL METHOD FOR GOCI OCEAN COLOR SENSOR

  • Moon, Jeong-Eon;Ahn, Yu-Hwan;Ryu, Joo-Hyung;Choi, Joong-Ki
    • Proceedings of the KSRS Conference
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    • 2007.10a
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    • pp.49-52
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    • 2007
  • This study uses empirical method to estimate absorption coefficient of colored dissolved organic matter $(a_{dom})$ from GOCI satellite data with the relationship between band ratio of remote sensing reflectance $(R_{rs})$ and $a_{dom}$. For development of $a_{dom}$ estimation algorithm, the used data is in-situ data about ocean optical properties in the around seawater area of the Korean Peninsula during 1998 - 2005. The relationship of $R_{rs}$(412)/$R_{rs}$(555), $R_{rs}$(443)/$R_{rs}$(555), $R_{rs}$(490)/$R_{rs}$(555), $R_{rs}$(510)/$R_{rs}$(555) and $a_{dom}$(412) showed $R^2$ values of 0.707, 0.707, 0.597 and 0.552, respectively. The spectrum of $a_{dom}({\lambda})$ is shape of exponential function $a_{dom}({\lambda})$ value decreases with increasing wavelength. For estimation of $a_{dom}$ from satellite data, we developed an algorithm from the relationship of $a_{dom}$(412) and $R_{rs}$(412)/$R_{rs}$(555). This algorithm was employed on SeaWiFS imagery to estimate $R_{rs}$(412) in the South Sea, East Sea, Yellow Sea and northern East China Sea areas. Also, SeaDAS-derived $a_{dg}$(412) from same SeaWiFS imagery, These $a_{dg}$(412) was then compared with in-situ and empirical-algorithm-derived $a_{dom}$(412), but these values were different. We think two points that such different values are caused by discrepancy related to failure of standard atmospheric correction scheme, the other are caused by error related to definition of $a_{dom}$(412) and $a_{dg}$(412).

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